Update knn.py

knn.py

@@ -6,17 +6,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 def distance_matrix(A, B):
-    """
-    Compute the L2 Euclidean distance matrix between two matrices A and B.
     
-    Parameters:
-        A (numpy.ndarray): Matrix of shape (m, n)
-        B (numpy.ndarray): Matrix of shape (p, n)
-
-    Returns:
-        numpy.ndarray: Distance matrix of shape (m, p) where the element (i, j) is the
-                       Euclidean distance between A[i] and B[j].
-    """
     # Squared norms of each row in A and B
     A_squared = np.sum(A**2, axis=1).reshape(-1, 1)  # Shape (m, 1)
     B_squared = np.sum(B**2, axis=1).reshape(1, -1)  # Shape (1, p)
@@ -55,19 +45,7 @@ def evaluate_knn(data_train, labels_train, data_test, labels_tests, k):
     accuracy = (labels_tests == result_test).sum() / N
     return(accuracy)
 
-# def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
-#     dists = distance_matrix(data_test, data_train)
-#     # Determine the number of images in data_test
-#     tot = len(data_test)
-#     accurate = 0
-#     predicted_labels = knn_predict(dists, labels_train, k)
-#     # Count the number of images in data_test whose label has been estimated correctly
-#     for i in range(tot):
-#         if predicted_labels[i] == labels_test[i]:
-#             accurate += 1
-#     # Calculate the classification rate
-#     accuracy = accurate/tot
-#     return accuracy
+
 
 
 if __name__ == "__main__":
@@ -88,15 +66,3 @@ if __name__ == "__main__":
     print(accurancy)
     
     
-#     data, labels = read_cifar('data\cifar-10-batches-py')
-   
-    
-#     data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
-
-#     k=3
-#     accurancies = []
-    
-#     accurancy = evaluate_knn(data_train, data_test, labels_train, labels_test, k)
-#     accurancies.append(accurancy)
-    
-#     print(accurancies)
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